samplers

A variety of samplers.

These different samplers implement basic sampling techniques including Full Factorial sampling, Latin Hypercube sampling, and Monte Carlo sampling.

class ema_workbench.em_framework.samplers.AbstractSampler

Abstract base class from which different samplers can be derived.

In the simplest cases, only the sample method needs to be overwritten. generate_designs` is the only method called from outside. The other methods are used internally to generate the designs.

abstractmethod generate_samples(parameters: ParameterMap | Iterable[Parameter], size: int, rng: NumpySeedLike | RNGLike | None = None, **kwargs) → SampleCollection

Generate n samples from the parameters.

Parameters:

  • parameters (collection) – a collection of RealParameter, IntegerParameter, and CategoricalParameter instances.

  • size (int) – the number of samples to generate.

  • rng (numpy random number generator)

  • kwargs (any additional keyword arguments)

Return type:

numpy array with samples

class ema_workbench.em_framework.samplers.FullFactorialSampler

Generates a full factorial sample.

If the parameter is non-categorical, the resolution is set the number of samples. If the parameter is categorical, the specified value for samples will be ignored and each category will be used instead.

generate_samples(parameters: ParameterMap | Iterable[Parameter], size: int, rng: NumpySeedLike | RNGLike | None = None, **kwargs) → SampleCollection

Generate samples using full factorial sampling.

Parameters:

  • parameters (collection) – a collection of RealParameter, IntegerParameter, and CategoricalParameter instances.

  • size (int) – the number of samples to generate.

  • rng (numpy random number generator)

  • kwargs (any additional keyword arguments)

  • sampler. (There are no additional valid keyword arguments for the Monte Carlo)

class ema_workbench.em_framework.samplers.LHSSampler

generates a Latin Hypercube sample over the parameters.

generate_samples(parameters: ParameterMap, size: int, rng: NumpySeedLike | RNGLike | None = None, **kwargs) → SampleCollection

Generate samples using latin hypercube sampling.

Parameters:

  • parameters (collection) – a collection of RealParameter, IntegerParameter, and CategoricalParameter instances.

  • size (int) – the number of samples to generate.

  • rng (numpy random number generator)

  • kwargs (any additional keyword arguments)

  • are (Additional valid keyword arguments)

  • scramble (bool, optional)

  • optimization ({None, "random-cd", "lloyd"}, optional)

  • strength ({1, 2}, optional)

Return type:

numpy array with samples

class ema_workbench.em_framework.samplers.MonteCarloSampler

Monte Carlo sampler for each of the parameters.

generate_samples(parameters: ParameterMap | Iterable[Parameter], size: int, rng: NumpySeedLike | RNGLike | None = None, **kwargs) → SampleCollection

Generate samples using Monte Carlo sampling.

Parameters:

  • parameters (collection) – a collection of RealParameter, IntegerParameter, and CategoricalParameter instances.

  • size (int) – the number of samples to generate.

  • rng (numpy random number generator)

  • kwargs (any additional keyword arguments)

  • sampler. (There are no additional valid keyword arguments for the Monte Carlo)